# HyperLogLog Functions¶

Presto implements the `approx_distinct()`

function using the
HyperLogLog data structure.

## Data Structures¶

Presto implements HyperLogLog data sketches as a set of 32-bit buckets which
store a *maximum hash*. They can be stored sparsely (as a map from bucket ID
to bucket), or densely (as a contiguous memory block). The HyperLogLog data
structure starts as the sparse representation, switching to dense when it is
more efficient. The P4HyperLogLog structure is initialized densely and
remains dense for its lifetime.

HyperLogLog implicitly casts to P4HyperLogLog,
while one can explicitly cast `HyperLogLog`

to `P4HyperLogLog`

:

```
cast(hll AS P4HyperLogLog)
```

## Serialization¶

Data sketches can be serialized to and deserialized from `varbinary`

. This
allows them to be stored for later use. Combined with the ability to merge
multiple sketches, this allows one to calculate :func:!approx_distinct` of the
elements of a partition of a query, then for the entirety of a query with very
little cost.

For example, calculating the `HyperLogLog`

for daily unique users will allow
weekly or monthly unique users to be calculated incrementally by combining the
dailies. This is similar to computing weekly revenue by summing daily revenue.
Uses of `approx_distinct()`

with `GROUPING SETS`

can be converted to use
`HyperLogLog`

. Examples:

```
CREATE TABLE visit_summaries (
visit_date date,
hll varbinary
);
INSERT INTO visit_summaries
SELECT visit_date, cast(approx_set(user_id) AS varbinary)
FROM user_visits
GROUP BY visit_date;
SELECT cardinality(merge(cast(hll AS HyperLogLog))) AS weekly_unique_users
FROM visit_summaries
WHERE visit_date >= current_date - interval '7' day;
```

## Functions¶

- approx_set(x) -> HyperLogLog()¶
Returns the

`HyperLogLog`

sketch of the input data set of`x`

. The value of the maximum standard error is defaulted to`0.01625`

. This data sketch underlies`approx_distinct()`

and can be stored and used later by calling`cardinality()`

.

- approx_set(x, e) -> HyperLogLog()¶
Returns the

`HyperLogLog`

sketch of the input data set of`x`

, with a maximum standard error of`e`

. The current implementation of this function requires that`e`

be in the range of`[0.0040625, 0.26000]`

. This data sketch underlies`approx_distinct()`

and can be stored and used later by calling`cardinality()`

.

- cardinality(hll) -> bigint()
This will perform

`approx_distinct()`

on the data summarized by the`hll`

HyperLogLog data sketch.

- empty_approx_set() -> HyperLogLog()¶
Returns an empty

`HyperLogLog`

. The value of the maximum standard error is defaulted to`0.01625`

.

- empty_approx_set(e) -> HyperLogLog()¶
Returns an empty

`HyperLogLog`

with a maximum standard error of`e`

. The current implementation of this function requires that`e`

be in the range of`[0.0040625, 0.26000]`

.

- merge(HyperLogLog) -> HyperLogLog()¶
Returns the

`HyperLogLog`

of the aggregate union of the individual`hll`

HyperLogLog structures.

- merge_hll(array(HyperLogLog)) -> HyperLogLog()¶
Returns the

`HyperLogLog`

of the union of an array`hll`

HyperLogLog structures.